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Backpropagation: past and future
Abstract
Some scientists have concluded that backpropagation is a specialized method for pattern classification, of little relevance to broader problems, to parallel computing, or to our understanding of the human brain. The author questions these beliefs and proposes development of a general theory of intelligence in which backpropagation and comparisons to the brain play a central role. He also points to a series of intermediate steps and applications leading up to the construction of such generalized systems, including past applications to social science which in some ways go beyond the work in AI as such. The author presents a condensed mathematical summary of that work. He begins by summarizing a generalized formulation of backpropagation, and then discusses network architectures and applications which it opens up.>
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Citations
Cited by
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
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Lewis, F. L., Yesildirek, A., & Liu, K. (1996) · IEEE Transactions on Neural Networks · doi:10.1109/72.485674 · Cited by 1,114
Control of a nonholonomic mobile robot using neural networks
Fierro, R., & Lewis, F. L. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.701173 · Cited by 717
Neural net robot controller with guaranteed tracking performance
Lewis, F. L., Liu, K., & Yesildirek, A. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.377975 · Cited by 638
Improved Computation for Levenberg–Marquardt Training
Wilamowski, B. M., & Yu, H. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2045657 · Cited by 622
Metadata
- Authors
- Werbos
- Venue
- IEEE International Conference on Neural Networks
- Publisher
- IEEE
- Published
- 1988
- Pages
- 343-353 vol.1
- ISSN
- 1098-7576 (ISSN-L)
- OpenAlex
- W2153795254
- Cited by
- 228
- References
- 0
- FWCI
- 5.33
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Werbos. (1988). Backpropagation: past and future. IEEE International Conference on Neural Networks. https://doi.org/10.1109/icnn.1988.23866
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